Role: AI/ML Architect with Databricks , azure

Location: Los Angeles CA or New York NY (Hybrid or Remote)

Experience: 13+ Years

Contract/Fulltime

 Key Responsibilities:

AI/ML & Advanced Analytics

• Develop, train, and optimize ML models using Python, PySpark, MLflow, and Databricks Machine Learning.
• Conduct exploratory data analysis (EDA) to identify patterns, trends, and insights in large datasets.
• Deploy ML models into production using MLflow, Databricks Workflows, or other MLOps pipelines.
• Build analytics solutions such as forecasting, anomaly detection, segmentation, or recommendation systems.
• Design ML architectures aligned with Databricks Lakehouse on Azure.

Data Engineering & Lakehouse Architecture;

• Architect and build scalable ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows.
• Implement Delta Lake best practices, including OPTIMIZE, ZORDER, partitioning, and schema evolution.
• Design lakehouse layers (Bronze/Silver/Gold) with strong separation of compute and serving layers.
• Optimize cluster performance and jobs using Spark tuning, caching, and shuffle minimization.
• Work with multi-terabyte, time-series, high-velocity data in a distributed environment.
• Ensure robust data availability for downstream ML and analytics workloads.

AWS Cloud Integration:

• Architect end-to-end data and ML solutions using Azure services, including:
• S3 for storage
• IAM for identity & access
• Glue Catalog for metadata management
• Networking for secure, high-throughput data movement
• Integrate Databricks with AWS-native compute, API layers, and low-latency endpoints.

Business Collaboration & Leadership:

• Translate business problems into scalable analytical or ML architectures.
• Communicate complex statistical and architectural concepts to non-technical stakeholders.
• Collaborate with product, engineering, and business leaders to drive data-informed initiatives.
• Provide design leadership while remaining hands-on in execution.

Skills & Qualifications Required:

• Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Statistics, or related field.
• 10+ years of experience in data engineering, ML engineering, or AI/ML architecture roles.
• Deep expertise in Databricks on AWS, including:
• PySpark / Spark SQL